import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo

from fedml.model.cv.batchnorm_utils import SynchronizedBatchNorm2d


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, dilation=dilation, padding=dilation, bias=False
        )
        self.bn2 = BatchNorm(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, output_stride, BatchNorm, model_name, pretrained=True):
        self.inplanes = 64
        super(ResNet, self).__init__()

        self.model_name = model_name

        blocks = [1, 2, 4]

        if self.model_name == "deeplabV3_plus":

            if output_stride == 16:
                strides = [1, 2, 2, 1]
                dilations = [1, 1, 1, 2]

            elif output_stride == 8:
                strides = [1, 2, 1, 1]
                dilations = [1, 1, 2, 4]

            else:
                raise NotImplementedError

        elif self.model_name == "unet":
            strides = [1, 2, 2, 2]
            dilations = [1, 1, 1, 2]

        # Modules

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = BatchNorm(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(
            block, 64, layers[0], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm
        )
        self.layer2 = self._make_layer(
            block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm
        )
        self.layer3 = self._make_layer(
            block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm
        )
        self.layer4 = self._make_MG_unit(
            block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm
        )
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
        self._init_weight()

        if pretrained:
            self._load_pretrained_model()

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                BatchNorm(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation, downsample, BatchNorm))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm))

        return nn.Sequential(*layers)

    def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                BatchNorm(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes, planes, stride, dilation=blocks[0] * dilation, downsample=downsample, BatchNorm=BatchNorm
            )
        )
        self.inplanes = planes * block.expansion
        for i in range(1, len(blocks)):
            layers.append(block(self.inplanes, planes, stride=1, dilation=blocks[i] * dilation, BatchNorm=BatchNorm))

        return nn.Sequential(*layers)

    def forward(self, input):
        if self.model_name == "deeplabV3_plus":
            x = self.conv1(input)
            x = self.bn1(x)
            x = self.relu(x)
            x = self.maxpool(x)

            x = self.layer1(x)
            low_level_feat = x
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            return x, low_level_feat

        elif self.model_name == "unet":
            x = input.detach().clone()
            stages = [
                nn.Identity(),
                nn.Sequential(self.conv1, self.bn1, self.relu),
                nn.Sequential(self.maxpool, self.layer1),
                self.layer2,
                self.layer3,
                self.layer4,
            ]

            features = []
            for i in range(len(stages)):
                x = stages[i](x)
                # print("In resnet ",x.shape)
                features.append(x)

            return features

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, SynchronizedBatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _load_pretrained_model(self):
        pretrain_dict = model_zoo.load_url("https://download.pytorch.org/models/resnet101-5d3b4d8f.pth")
        model_dict = {}
        state_dict = self.state_dict()
        for k, v in pretrain_dict.items():
            if k in state_dict:
                model_dict[k] = v
        state_dict.update(model_dict)
        self.load_state_dict(state_dict)


def ResNet101(output_stride, BatchNorm, model_name, pretrained=True):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], output_stride, BatchNorm, model_name, pretrained=True)
    return model


if __name__ == "__main__":
    import torch

    model = ResNet101(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=8)
    input = torch.rand(1, 3, 512, 512)
    output, low_level_feat = model(input)
    print(output.size())
    print(low_level_feat.size())
